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testing (NDT), and the mechanics of advanced materials, particularly composites and fatigue-critical structures. A key research direction involves the integration of machine learning and deep learning
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and geometric deep learning, or simulation-based inference. We welcome your unique perspective and are eager to learn how your track record, educational vision, and future research goals align with
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atmospheric perturbations, and improving performance under realistic operational conditions. Main activities include: • Designing and developing deep learning models to correct wavefront sensor nonlinearities
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course development at first and second-cycle levels related to AI (with and without ties to cybersecurity), particularly deep learning, generative AI, large multimodal models, diffusion models and deep
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analysis in cryo-electron tomography data, applied to chromatin organization and synaptic molecular targets. Please include a cover letter with your application. Describe a deep learning project you have
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letter with your application. Describe a deep learning project you have executed, ideally involving 3D image analysis, inverse problems, or physics-informed modeling. Cryo-EM/ET and computational
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authority and leadership capability to drive meaningful change. You will bring: A PhD or equivalent qualification, with a distinguished record of leadership in teaching and learning at the tertiary level. An
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Deployment Strategies - Model Compression: Investigate techniques such as quantization, pruning, and knowledge distillation to reduce the computational and memory footprint of deep learning models without
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for supply chain and marketing optimization. The project will integrate machine learning, deep learning, foundation models, and interpretable AI approaches, ensuring scalability, robustness, and industrial
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the competent authority of the Ministry of Higher Education, Research and Innovation (MESR). "Video content security in a deep learning coding architecture" Over the past few decades, numerous video compression